if what look like displacements violate island constraints but are still acceptable, this means that they should not be derived by movement.


11 It is assumed that in Japanese, nominative Case checking should be done in the specifier of vP without movement to the specifier of TP (see Fukui, 1995;Kuroda, 1992).

a subject does not move to the specifier position of TP unless T has an EPP feature (cf.


18 There is no way in my proposed analysis to exclude examples such as (i):  19 For more details and many more examples, see Kamada (2009).

type I: β contains α; type II: β ccommands α; and type III: β neither contains nor c-commands α. I will begin with the type shown in (10a).


According to the UREC in (8), however, the complex NP has priority over the null argument for association with the PVE, because the complex NP contains the null argument and they are non-distinct in terms of categorial features and Case features.

the parser cannot associate the PVE with the null argument.


Consider the sentence in (19 In (19), Hanako-ga 'Hanako-NOM' is incorrectly analyzed as an element in the embedded clause.

hanako-ga is construed as an argument of sonkeisiteiru 'respect.'


Thus, there are no appropriate elements with which the PVE can be associated.

the PVE is difficult to associate with the null object in the embedded clause.


the clause is not similar to the null argument in the sense of (9).


The matrix verb mita 'saw' can be modified by the locative PP, and it also contains the complex NP that includes the other verb atta 'met with;' hence, the matrix verb is chosen as a modifiee over the embedded one.

the postverbal locative PP is difficult to associate with the verb at-ta 'met with' within the relative clause.


This ambiguity can be derived from the UREC in (8).


Because an increasing number of corpora have been constructed, and each corpus is constructed for a definite purpose, this study identifies the functions of corpora and combines the values of various types of corpora for auto-learning based on the existing corpora.

the following three corpora are adopted: (a) the Corpus of Spanish; (b) the Corpus of Taiwanese Learners of Spanish; and (c) the Parallel Corpus of Spanish, English, and Chinese.


Japanese kana can perfectly correspond to Chinese characters one by one.

one Japanese kana can be presented by one Chinese character.


Those studies determined an EDU differently.


Of course, the main reason for this unfortunate tradition is due to the fact that clitics are not independent phonologically.

the very nature of the language itself is partly responsible for such a tradition.


(7) cyon-i kongpwu(-lul) cal/manhi/… -Acc well/much/… ha-ko iss-ta 'John is studying well/much/…' Judging from the data in (7), which show that external elements can be inserted between kongpwu and ha-, it becomes clear that ha-is a word and [kongpwu ha-] is a phrase.

kongpwu and ha-are two independent words (Song 1967, Suh 1991, Chae 1996 Chong 2011, among others).


-n-and -nun-, are just parts of the (present) declarative endings of verbs in the plain speech level.

we can assume that -nta and -nunta are "portmanteau" morphs, 7 i.e.


As shown in Figure 1, it is located mid-region along the cline of instantiation, between the potential pole of the overall (collective) meaning potential of a language and the instance pole of instantial acts of meaning unfolding to make up texts in context.

we observe registerial variation (like any other kind of variation) as selections in texts as they unfold in their contexts of situation, and when we try to generalize these selections as recurrent patterns of selection, we find that the generalized patterns of selection are located mid-region along the cline of instantiation.


Halliday, 1978Halliday, : 130-131, 1996Halliday & Matthiessen, 2013: 48-49), supplementing the view "from above" -from contexts, with the views "from below" -from lexicogrammar and phonology (or graphology), and "from roundabout" -from the level of semantics itself, the level at which the variation takes place in the first instance (in terms of the "meanings at risk" in different contexts).

the project of registerial cartography includes centrally stratal coverage in the account of registers, from the contexts in which they operate to the linguistic strata where their semantic patterns are realized; stratal coverage thus includes a chain of inter-stratal realizations: context to semantics, semantics to lexicogrammar, and lexicogrammar to phonology or graphology (cf.


In summary, I have reported on aspects of our research into registers -our long-term research programme of registerial cartography.

i have discussed the relation between fields of activity within context, logico-semantic relations used to form rhetorical nexuses in the (global) semantic organization of text, and the mode of the lexicogrammatical realizations of these relations.


In this paper we raise these questions on the basis of Korean newspaper corpora and some lists of 'Top Ten News (T10N for short)' compiled, presumably introspectively, by the newspaper editors at the end of every year.

we compare the top 100 keywords with T10N, and see how well they converge with each other.


It certainly would involve processes of understanding, interpretation, and abstraction on the part of the person that does the compiling job.

they represent human interpretation and understanding of the phenomena, i.e., the culture.


It is mediated, communicated, and created through discourse.

introspection based human keywords are not created from nothing; ideally they should reflect well the culture or society in question, and the discourse that constitutes and represents the society.


We will assume one of such case can be provided by news texts of a certain period of time.

the top ten news summarize a year's major events which had been reported in the news articles of the year.


For the experiment in this paper we mainly use a subset of the corpus, primarily the data that cover the four year span (2006)(2007)(2008)(2009) of a newspaper, Chosun, referring to a larger set when necessary.

we take the news texts of 2009 of Chosun as the target corpus, and those of 2006-8 of the same newspaper as the reference corpus.


According to Pylkkänen's argumentation, modifiers such as the PP in (2a) are verbal modifiers.

they can syntactically attach to verbal projections, but because they are not root modifiers, they cannot attach to the √P.


The learning mechanism will then not use this 'ambiguous input' to learn a parameter setting.

the learning mechanism will wait until an unambiguous trigger occurs in the input before setting any parameter value.


If take advantage of is a constituent, the preposition of cannot assign Case to Mary's honesty, and furthermore, nor can the passive morpheme -en assign Case to it.

in (9a) Mary's honesty occurs in a Caseless position, and it needs to move to a position where it can be assigned Case.


However, it is worthwhile to reiterate that even the verbal passive can produce the pseudo-passive with the help of modals such as can, when the Theme is not affected.

speaking, the pseudo-passive is an adjectival passive when its Theme is affected, and it is a verbal passive when its Theme is not affected.


b. John asked me why Mary bought what, but John didn't ask me how Susan did.

in 10awhat John read may be referentially different from what Bill did.


word) vectors u ∈ R d 1 and v ∈ R d 1 , the model f yields a phrase vector p ∈ R d 2 as a composition of the input vectors.

the model f is a function that computes a phrase vector p for the inputs u and v. Setting generating phrase or sentence vectors consisting of three or more words.


(2006), and retaining tokens that make up 90% of the accumulated frequency.

the dictionary can cover up to 90% of the tokens in the corpus.


However, correcting language data for all target topics is demanding and difficult.

when each target topic becomes narrower and the number of target topics increases, it becomes impractical to correct language data for all topics.


Our work extends the idea of zero-shot learning to language models, which have more complex structure than class labels by exploiting the semantic compositionality of complex topics.

our work goes beyond the word level and treats the sentence level structure.


Japanese also allows "multiple stripping."

the pre-copula part may involve more than one NP: (2) A: Tom-ga ringo-o tabe-ta-yo.


Note that we have parsed the antecedent (12)A.

when hashi (= 'run') was processed, a propositional structure with a subject slot was built.


24  (25)B is grammatical since every unfixed node is immediately resolved by a particle.

there is only a single unfixed node at a time.


Case-less sluicing: The tree state (33) holds even when the case particle ga is not attached to the whphrase dare.

irrespective of case-marking, uniformity in our analysis remains intact.


In addition, our study is novel in adopting the multistage approach to solving word problems automatically.

this paper makes the following contributions.


While our MWP solver had an accuracy of 86% on the sample, the performance of Wolfram Alpha is remarkably poor.

barely 9% of the problems were answered correctly, of which about 4% had an incorrect diagram associated with the solution.


However, we will demonstrate in the next section that Thai ISPs do not exhibit the "person restriction" pointed out for Japanese ISPs.

the pattern observed for the second person in (2) is not a property of the "person restriction" per se.


This is because, under our new characterization of the constraint, the omniscient narrator as the conceptualizer knows the experiences of any character in the story to the effect that she can be identical with the experiencer of these internal states.

it is not that the restriction is "lifted" under some condition, but rather that the ECI constraint takes effect in the case of an omniscient narrator in the literary mode.


7 Japanese also possesses a grammatical construction, namely, the no-da construction, which, when used with ISPs, serves to lift the ECI constraint and make them behave like their counterpart ISPs in Thai.

this no-da morpheme has the ECI constraint-lifting function.


In Japanese the ECI constraint is conventionalized at the lexical level, whereas in Thai it is conventionalized at the grammatical level.

the two languages differ in how the ECI constraint is linguistically conventionalized.


Yet, antonymy is essential for many Natural Language Processing (NLP) applications, such as Information Retrieval (IR), Ontology Learning (OL), Machine Translation (MT), Sentiment Analysis (SA) and Dialogue Systems (Roth and Schulte im Walde, 2014;Mohammad et al., 2013).

the automatic identification of semantic opposition is a crucial component for the detection and generation of paraphrases (Marton et al., 2011), the understanding of contradictions (de Marneffe et al., 2008) and the detection of humor (Mihalcea and Strapparava, 2005).


However, these models are characterized by a major shortcoming.

they are not able to discriminate among different kinds of semantic relations linking distributionally similar lexemes.


However, according to Cruse's definition, we can expect one dimension of meaning in which the antonyms have different behaviors.


As for Thai, three important pioneering studies have revealed that Thai, like most tonal languages, is characterized by parallelism between the transition of lexical tones and the transitions between two adjacent musical notes.

tonal transitions and note transitions between adjacent syllables in Thai songs typically agree in direction.


As expected, the results in Table 3 show that ascending note transitions were mapped with tone pairs with a higher second tone.

cases of MID → HIGH were mapped with ascending transition at a statistically significant level (p<0.001).


This indicates that RISING behaves like HIGH with respect to tonemelody mapping.

the RISING is treated as if it was HIGH.


From the phonological perspective, many phonologists, e.g.

gandour (1974a), Yip (1982) and Morén and Zsiga (2006), argue convincingly that contour tones in Thai are in fact made up of sequences of H and L. FALLINg and RISINg can be represented as [HL] and [LH] respectively.


More specifically, when tone pairs with identical first and second members occur successively, although they tend to map with musical level transition, the percentage of mapping with musical ascending and descending transitions is close to that of level transitions.

thai pop music composition does not strive to maximize parallel transitions but tries to avoid opposing ones.


Furthermore, the results also suggest a new way of looking at parallelism between tone transitions and musical note transitions.

they suggest that the composition of Thai pop songs places more importance on avoidance of opposing transitions than achieving parallel transitions.


#Tweets 276,970 103,740  To evaluate our automatic corpus construction method, we performed two tasks, namely opinion tweet extraction and tweet polarity classification, harnessing our constructed training data.

we see whether or not a classifier model trained on our constructed training data is able to peform both the aforementioned tasks with high performance.


Recently, with the advent of social-media, it has become easy to express opinions or comment about experiences.

twitter 1 is a popular service used worldwide, and extremely large number of messages (tweets) is generated every day on it.


Based on this insight, we introduce a weighted frequency function by using time span information between two tweets.

equation (4) represents the alternative weighted frequency function f ′ (w, c f i ), which is used in equation 2and equation 3 The set W indicates an instance set of w existing in c f i , and the symbol e indicates an element in W. f (w, c f i ) = |W|.


Word scores are estimated based on the assumption that the polarities of words are coincident with the polarity of their associated sentences, which seems reasonable due to the short length of tweet messages.

if the word frequently appears in the positive (or negative) tweets, its polarity might be positive (or negative).


The adnominal case (of this/that) is analyzed by adding such a presupposition, while the Japanese pronominal cases can be analyzed by removing the presupposition that N (x) from each clause of (23).

the pronominal uses also have implications for the animacy/sentience of their referents; for instance, are/sore/kore in general cannot denote a sentient entity, and neither can pronominal this/that (except when they occur as the subject of be, as in: his assistant.


Several studies contend that the main motivation for scrambling is heaviness.

yamashita (2002) maintains that scrambling has nothing to do with givenness and that heaviness is the primary factor for scrambling.


They differ not in what is said about the world, but in the way it is packaged (Chafe, 1976;Lambrecht, 1996;Vallduvi and Engdahl, 1996).

their differences derive from information structure, i.e.


how the meaning of a sentence is conveyed.

it has long been recognized since the work of the Prague School that speakers prefer to put given information before new information.


information mentioned in the preceding discourse.

previously mentioned constituents are considered to be given information.


What I should emphasize here is that RD is a quantitative value and has several measures to assess degrees of givenness.

it is possible to state that some referent is 1 The limitation of RD is rather arbitrary.


In her written Japanese data, heaviness accounts for about 70% of the scrambled sentences while referentiality makes up about 25%.

70% of scrambled objects are long and 25% of them include a determiner or an anaphor either referring to something appearing in the preceding discourse, or inferable from it.


OSV sentences were collected from BCCWJ by using Chunagon, which is a web interface program.

the string [o(ACC)-noun-ga(NOM)] was used to extract OSV examples.


As for givenness, some kinds of inferable information are categorized into discourse-old information.

bridging relations are taken into consideration.


In contrast, in (7b), V1 and V2 have different subjects.

v1 motte 'have' forms a nexus with tomodachi 'friend' and v2 karite 'borrow' forms a nexus with the zero subject 'I'.


In contrast, with regard to S-DO-IO-V order, Ferreira and Yoshita (2003) observed that there was no interaction between given-new ordering and scrambling in sentence production.

givenness has no strong influence on the choice of S-DO-IO-V word order.


This is because sentence-initial position can have a relationship with information-related projections in the CP-zone.

it may have a strong relationship with TOPIC projection, which seems to be pertinent to given information.


In this study, I have demonstrated that givenness has an influence on OSV order, being independent of heaviness.

it has been shown that the discourse-status of a scrambled object is important for the usage of OSV; OSV is preferable when O is discourse-old information.


In order to examine the validity of these hypotheses, further corpus data are needed.

it is necessary to check the heaviness effects and the givenness effects on S-DO-IO-V.


In relation to that, annotators should be informed about the halo effect, by which the judgement of a sentence as acceptable or unacceptable is influenced by our overall impression of previous sentences.

one is more likely to find errors in a text if this text already contains other errors.


Therefore, more care should be paid to pragmatic distinctions.

disagreement is more likely in noun phrases where two pragmatic interpretations (and article choices) are possible, and annotators choose one of the alternatives in an inconsistent manner ( § 4.3.1 and 4.3.2).


Concretamente los cursos que consiguieron participantes japoneses y que ofrecen los certificados oficiales como IMEC(Instituto de Medicina China) continuarán existiendo [...].

'the courses which obtained Japanese participants and offer official certificates like IMEC (Chinese Medicine Institute) will continue existing [...].'


If the feature 'f3' which is about number of the verb is eliminated, the accuracy decreased about 2%.

the 5 low rank features may be regarded as not significant ones to classify subject types.


Incremental training provides a way to perform faster, more frequent model updates, enabling keeping the SMT system up-to-date with the most recent data.

we address incrementally updating the reordering model (RM), a component in phrase-based machine translation that models phrase order changes between the source and the target languages, and for which incremental training has not been proposed so far.


As mentioned, B is typically much smaller than A: |P B | ⌧ |P A |, and the merged set is at least as large as the old one.

|P AB | |P A |, and P AB is strictly larger than P A if any new phrase pairs are found in the new data relative to the older one.


R AB 7:  In this section we evaluate updating the reordering model from two aspects: (i) translation performance and (ii) run-time.

we first show that updating this model helps improving translation, as reflected in the BLEU score (Papineni et al., 2002); then we show that the incremental update is faster than the complete one.


A probabilistic model predicts assimilation outcomes not based on the distance, but on the instances on the input of L2 phones, and its probabilistic balance with regard to L2.

l2 learning is statistical learning instead of a mere calculation of distances.


In addition to the Congruence Principle, Andersen & Shirai (1994) have also proposed the Distributional Bias Hypothesis which points out that in adult native speakers' language the perfective past inflections occur more often on Accomplishments and Achievements than on Statives and Activities.

the input to learners exhibits, in relatively quantitative terms, similar distributional imbalance.


Most studies along this line of research have focused on the acquisition of a certain European language by native speakers of another European language.

the L1s and the L2s involved in most studies are typologically similar languages.


However, most of the errors our L2 Chinese learners make with Statives and  Activities are just the opposite: using the perfective marker -le when it is not needed.

the problem predicted by Claim 1 is UNDER-USE of the perfective marking on Statives and Activities and the L2 learners of Chinese display no UNDER-USE of the perfective marking on these situation types due to the fact that -le is usually NOT required on situations without boundaries.


The past location indicated by the English simple past in fact provides an arbitrary temporal endpoint to any situation it marks.

we assume that this is the reason why the perfective past can be used on verbs of all types in English: the Relevance Principle is also observed in English but in a different way.


In this paper, we propose a superior model named TransM which aims at leveraging the structure information of the knowledge graph.

speaking, we keep the transition-based modeling for triplets proposed by TransE (Bordes et al., 2013b;Bordes et al., 2013a), i.e.


For example, link prediction is a valuable task that contributes to completing the knowledge graph.

it aims at predicting the missing entity or the relationship given the other two elements in a fragmented triplet.


The counts returned by Google for "lions are predators" are significantly higher than those of "zebras are predators".

the system could resolve they to lions without considering the relationship between two clauses.


366 The source sentence (20) was constructed by using the background knowledge that the noun coach has a higher status than the noun captain in a team environment.

someone who has a higher status can fire other people.


Tang (1996) analyzes saai3 as a marker of distribution.

saai3 marks distributive plural sets of either events or internal arguments, but not subjects.


376 8 Implications As the data show that gwo3 can in fact be interpreted with the same syntax and semantics in both event-denoting and property-denoting predicates, this means that the cross-categorial behaviors of gwo3 can be explained with a homomorphic approach.

the semantics across categories can be structured in the same way.


that's right, that's true, that's correct and their variations based on the Switchboard Dialogue Act Corpus.

it offers an overview to account for how they are deployed by native speakers in daily conversation.


A glance at the table establishes that these top five functions together account for a large proportion among a series of functions performed by each particular set.

accept overwhelmingly occurs in all the three sets, followed by assessment/appreciation.


Overall, the distribution here shares a large number of similarities with that of set (1) in Table 5.

accept, assessment/appreciation and statement are considerably significant, while affirmative answer and acknowledgement are comparatively less crucial, only occurring in that's true, that's + adverb + true and true.


Moreover, that's true shows much greater likelihood to serve statement whereas that's right is almost ten times more likely than that's true to be acknowledgement.

when the preceding utterance is a statement or a question, the current utterance is more likely to serve statement if it is realized by that's true; it has greater possibility to be acknowledgement or an affirmative answer if it is realized by that's right.


that's right, that's true, that's correct) and their variations in the Switchboard Dialogue Act Corpus.

it offered an overview to account for how they are used in daily conversation with empirical evidence.


Unlike native speakers, the listening proficiency greatly differs among individuals (Saville-Troike 2006).

listening material can be appropriate for a learner but not for another.


Learners were equally divided into three groups of TOEIC scores: low score group (below 475), middle score group (from 480 to 725), and high score group (above 730).


According to this definition, we can recognize sarcasm by evaluating the polarity of the sentences.

a sarcastic sentence contains two or more words, which may cause conflict in sentiment polarities (both positive and negative) in a sentence, whereas a normal sentence should contain at most one polarity.


However, our approach can recognize it as a sarcastic tweet by using the concept level knowledge.

we can know "bad weather" is one of the related concepts of "raining" from an extra lexical resource, then we have a new concept "bad" (negative) together with the original word "better" (positive) to catch the contradiction in sentiment polarity in the sentence.


In texts, punctuation plays a vital role in text communication to provide the reader the signals about pause, stop and change of tone of voice.

let us consider an example sentence "very annoying!".


To collect sarcastic tweets, the hashtag "#sarcasm" is used.

tweets with #sarcasm are retrieved via Twitter 1 http://twitter4j.org/en/index.html API.


However, when coherence in the tweet is checked, our method will recognize that the words "My gf's mac", "I" and "Windows" are not related to each other.

coherence does not exist within the tweet.


kuno (1972: 277) illustrates the usage of GA and WA by citing (2).


previously mentioned) when it has a contrastive meaning.

contrastive WA can be both given information and new information.


For instance, (4b) needs more specific contexts than (4a) does.

native Japanese speakers feel that the topicalized object, sono-ringo "the apple", in (4b) should be interpreted as contrastive while there is no such constraint for the accusative object in (4a).


First, there was an interaction between the informational factor and the morphological factor.

this was caused by the fact that given-new ordering facilitated the processing cost of OACC SNOM V, but not the cost of SNOM OACC V, StOP OACC V, and OtOP SNOM V. only scrambled sentences were affected by give-new ordering.


This coincides with previous studies that claim the marked pattern to occur only in the licensing context, whereas the unmarked pattern is contextually unrestricted (Aissen, 1992;Birner and Ward 2009;Kuno, 1995).

birner and Ward (2009) point out that canonical word order can be used in a wide range of contexts while noncanonical word orders can be permitted only in a specific context.


In the new-given condition, the reaction times were not slowed down and the error rates did not become higher than in the givennew condition.

sOV was not penalized even in an inappropriate context.


Indeed, OACCANOMV was processed slower in the new-given condition than in the given-new condition.

oACCSNoMV was penalized in the new-given condition and this is why it was processed slower than in the given-new condition.


However, we feel that there is a difference between them concerning the fact whether the structure is context dependent (as, for example, because of this increase) or not (as because of this).

because of this increase may be used only in a limited set of contexts (in texts about increasing), but because of this is context independentthis is a deictic word so it may be embedded to any context and it will find there its semantic relations.


whether they function as indicators of certain discourse relation universally or occasionally.

we tried to answer -if we have several different contexts with, e.g., the relation of reason and result -whether the given connective structure (with an ability to express this type of relation) fits into each of them (and is therefore universal) or not.


However, both sentences of example (6) are generic, which means there is no temporal order between the things described.

we can decide neither which fact occurred earlier nor which fact was recognized earlier.


(c) If B is an event and B happens successively to A, then tag the relation with the Narration label.

do so when B's reference point is just after A's reference point.


While ordinarily changes in speech level are determined by the external context (or so the model above has it), it is also the case that the use of honorifics can impact the formality level of the discourse continuation.

there are points at which it is obvious that the speech level should be changed; but sometimes the use of an informal form causes a switch to an informal level, although if the informal form had not been used, the level would not have changed.


(1) a. Taro This paper offers another piece of evidence for the virtual identity of topics and conditionals.

i argue that topics have the same semantics as conditional antecedents in that both serve as contextshifters.


Thus, the declarative construction marked with dake-wa as in (7-a) denotes exhaustification over assertion acts.

only the assertion of the prejacent proposition is executed and the rest of the alternative assertions are cancelled.


This section presents the data central to the current paper in detail.

the dake-wa 'only-TOPIC' construction is incompatible with question acts.


2 Thus, I propose that the Japanese topic-marker wa marks Austinian topics (Austin, 1950).

the topic-marked element denotes what an utterance is about.


(I don't make assertions about other individuals; only>assertion) Hence, dake generates alternative temporary contexts, 'if we are speaking of John', 'if we are speaking of Mary', etc., and the exhaustive component of dake conveys that the speaker is restricting her assertion to the proposition 'John came' with the discourse topic 'John': 16The utterance of 'John-dake-wa kita' is felicitous iff a.

s asserts 'John came'; and the truth-condition of (15) is the same as that of John-ga kita and John-wa kita, namely 'John came'.


The difference is the speaker's intention in the discourse.

in (15), the speaker is indicating that she is willing to make assertions only about John and the alternative speech acts about other individuals are cancelled.


5 To implement this approach to questions in a dynamic semantics, Isaacs and Rawlins adopt Groenendijk's (1999) analysis of questions, which defines the context set as an equivalence relation on worlds.

the context set is a set of pairs of worlds specifying a relation that is symmetric, transitive, and reflexive: 20Definition: context A context c is an equivalence relation on the set of possible worlds W .


6 At the initial stage, the conversational agent is ignorant about these issues.

the agent has no pre-existing commitments about facts or issues.


Since w 3 and w 4 resolve the question in different ways, the two worlds are disconnected.

the pairs that connect the two worlds are removed as in (30), and the temporary context is partitioned into two cells.


Why do issues, i.e., inquisitive contexts, not percolate down the stack?

why do questions not affect the other members of the stack?


(I don't make assertions about other individuals; only>assertion) When the F-feature of the dake-wa phrase is processed, the interpreter realizes that multiple stacks will be created.

a topic-marked F-feature denotes a macro-context change potential which creates a multi-stack and performs an update over the created multi-stack: Definition: For a macro-context s and a topicalized construction After the percolation, i.e., the assertive update on macro-contexts, the temporary contexts are popped from the entire multi-stack.


We present a new system, KEA (keyword extraction based sentence aligner), that automatically learns to align sentences, considering word keyness, of monolingual parallel articles.

kEA aligns texts in the same language at sentence level that are "translation" of each other with different readability.


And the sum of the propagation is further weighted by the word's degree of reference.

for the word w j in Content, its PageRank score is computed as where λ is referred as damping factor and usually set to 0.85.


The algorithm initializes a (m+1)-by-(n+1) matrix DP to store the optimal sentence alignment score.

dP[i+1,j+1] records the best score for aligning sentences between Sento,1,…, Sento,i and Sents,1,…, Sents,j (Step 2) where 1≤i≤m, the number of the sentences in Arto, and 1≤j≤n, the number of the sentences in Arts.


Compared to the frequent sentence re-structuring and re-ordering, re-ordering of words in sentences seldom happens in simplification.

words in the original sentences will be translated or simplified in order.


This test set had 1,098 original and 1,285 simplified sentences.

there were 17K words in the testing original articles while there were 14K words in the simplified.


Applied on monolingual parallel corpora, KEA with full capability outperforms the current stateof-the-art TF*IDF (Nelken and Shieber, 2006).

kEA further improves precision and f-measure relatively by 6.25% and 3%.


Rather, we aim to preserve the phrases in the sentence that can potentially result in comma splices and strip away the rest so that the parser has the best chance to produce the expected parse patterns.

using the parse tree of the original sentence, we remove (1) introductory phrases at the beginning of a sentence, which include transition phrases such as "for example", as well as prepositional phrases and adverbials 6 ; (2) clauses that are properly connected to the rest of the sentence by a coordinate conjunction; (3) subordinate clauses that are properly connected to the rest of a sentence by subordinate conjunctions or relative pronouns; and (4) dialogue tags such as "he claimed" or "he argued" 7 .


As summarized in Table 2, A-NOT-A questions can be used only in neutral contexts, while AA4 questions can be used in both neutral and biased contexts.

an A-NOT-A question explicitly encodes its neutrality requirement in the semantics while an AA4 question simply performs a question act.


However, Yuan and Hara's implementation of the neutrality requirement faces a conceptual problem for both Mandarin and Cantonese.

in truth-conditional semantics, p _ ¬p is equivalent to q _ ¬q since they are both tautologies thus always true.


As its primary speech act, it asserts p ( 0 ✏ + M p).

at the same time, as its secondary act, it indicates that ¬p is a prior expectation, which is now 'ad would not be in amsterdam' used to be expected, ✏ M ( 2 ) ✏ + M ¬p, but at some point it stopped being expected, Let us turn to an interrogative TOCH?, namely ?


Finally, I agree with Lam (2014a) in that A-NOT-A questions are neutral questions, though contra Lam (2014a), I propose that A-NOT-A questions are complex speech acts.

a-NOT-a questions are anti-bias questions.


In order to describe as well as explain the difference in uses between Beijing Mandarin and Taiwan Mandarin, we adopt the Theta System Theory (Reinhart 2002; Marelj 2004) to probe into the argument structures of the transitive verb fan and further pinpoint the fundamental syntactic difference between Beijing Mandarin and Taiwan Mandarin, that is, the absence or presence of the /+c feature in the argument structure.

taiwan Mandarin requires the obligatory presence of the /+c feature in the argument structure of fan, while Beijing Mandarin does not.


In addition, they seem to impose different restrictions of animacy on the object B.

[A fan B] in (7) requires B to be animate (in particular, Human) whereas [A fan B] in (8) and (9) features the inanimacy of B.


In this connection, one thing is worth noting.

the [-m] role, as an under-specified role, cannot bear the ACC feature.


That is, the [-m] role, as an under-specified role, cannot bear the ACC feature.

the mental fan is not an accusative case assigner.


The argument structures in (32) reveal that Beijing Mandarin and Taiwan Mandarin differ in the presence or absence of the [/+c] feature.

taiwan Mandarin treats /+c as an indispensable feature of the transitive verb fan.


Therefore, they are not exclusive to Taiwan Mandarin.

monosyllabic and disyllabic mental verbs are not in complementary distribution between Beijing Mandarin and Taiwan Mandarin.


This study examines spatial terms for vertical directions in Thai and English.

we focus on khɨn3 'ascend' and loŋ1 'descend' in Thai in comparison with up and down in English.


Secondly, these forms are thought of as mapping directly to meaning which is the ultimate object of study in discourse analysis.

constructions that capture local grammatical templates and word classes may be particularly useful.


In order to answer these questions, we analysed constructions in a corpus of climate change blogs.

we identified unusually frequent constructions in three major blogs (which can be considered as sub-corpora), and qualitatively evaluated the utility of these constructions for corpus-based discourse analysis.


That said, because the induction process exploits partially overlapping word sequences around key terms, we expect that it will be most effective on large corpora with relatively constrained language use.

it will work best with corpora that consist of a single domain and a single text genre.


A kernel is a similarity function between a pair of instances.

since string kernels are useful in terms of measuring the similarity of non-fixed size feature vectors (e.g., text documents, the dependency tree of a sentence, and syntax trees) (Erkan et al., 2007), we used a string kernel given that the features that consider the morphological structures of words are variable in length.


Inspired by Bresnan and colleagues' work on English syntactic variation, there have also been a few studies that apply a similar modeling approach to the study of syntactic variation in Chinese languages (Cantonese: Starr, 2015;Mandarin: Yao, 2014;Yao and Liu, 2010).

yao and colleagues (yao, 2014;yao and Liu, 2010) investigated both dative variation and BA-form variation in written Mandarin using data from the Academia Sinica corpus (Chen et al., 1996).


is the trans- lation function, bidirectional analogical deduction also requires to repeat this operation with all target translations corresponding to the source bigrams in the opposite direction.

satisfies following equation: We define "bidirectional reconstructible" as when input an unseen bigram and finally it outputs the solution as y.


As can be noticed from the presented results, after importing the features of templates which are used in analogy diveration, the performance of SVM classifier improves.

it means that the analogous information has the positive effects on classification.


SWSM uses the distribution of word senses that appear near the target word in WSD to estimate the word senses assuming that the word senses of the local context are differentiated depending on the word sense of the target word.

sWsM estimates the word sense according to p(s|w), which is a conditional probability of a string of senses, s, given a string of words w. sWsM involves three assumptions.


Therefore, SWSM counts the frequencies of all the possible word senses of the surrounding words in the corpus.

if there are polysemes A and B in the corpus and B is a surrounding word of A, SWSM counts the frequencies of the senses by considering that all the senses of B appeared near all the senses of A.


The row and column order of the matrix is placed depth-first in the RNN tree, right-to-left.

matrix element s 00 , which is the first element of similarity matrix S, is the degree of similarity between the left-most word vectors in each sentence.


In the well-known word2vec example, the vector operation "king -man + woman = queen" holds.

the difference vector "king -man " represents the information of kingship.


Compared with traditional paradigms based on solo knowledge base, DSEL benefits more via jointly leveraging the respective advantages of Freebase and Wikipedia.

the proposed paradigm facilitates bridging the disambiguated labels (Freebase) of entities and their textual descriptions (Wikipedia) for Web-scale entities.


Open IE identifies relational patterns and instances automatically without predefined target relations (Banko et al., 2007;Wu and Weld, 2010;Fader et al., 2011;Mausam et al., 2012).

open IE acquires knowledge to handle open domains.


We may probably not need exact counts of cooccurrences for representing pattern vectors because a small amount of elements in a pattern vector greatly influence the similarity computation.

it may be enough to find top-k entity pairs with larger counts of co-occurrences for each pattern, and to ignore other entity pairs with smaller counts.


A number of NLP researchers explored approaches to representing the meaning of a word with fixedlength vectors (Bengio et al., 2003;Mikolov et al., 2013).

word2vec 4 , an implementation of Mikolov et al.


We then manually checked some of the pattern pairs extracted from Wikipedia that were also contained within the 6.3 billion sentence corpus.

we first extracted frequent patterns from some domains in Wikipedia for obtaining the patterns representing a specific relation.


Therefore, there is a possibility that we obtain pattern pairs with a high similarity when both of the patterns contain one of the same entity types.

we need to encode not entities separately but maintaining entity pair as a pattern feature vector.


The plots show that, for similar words, the learned weights for the corresponding lexical features are only slightly similar; but after the lexical features are reduced to low-dimensional embedding features, the learned weights for the corresponding features are more strongly correlated.

weights for embedding features encourage similar behaviors between similar words, due to a much lower di-  mensionality.


For graph-based dependency parsing, it is not straightforward to extend the linear models to the more powerful nonlinear neural network, because we need to figure out the scores for the factors of the tree, which are not specified in the original treebank.

we only know which factors are in the correct parsing tree, but there are no natural ways to indicate how they are scored; the only intuition is to give high scores to the right factors and low scores to the wrong ones.


It is one of Eisner's models (Eisner, 1996).

eisner's model A is chosen and slightly modified for scoring.


And for o3g model, two lower-order scores are integrated.

the score for the factor (g, h, s, m) will include the lower-order scores of o1 and o2sib in addition to the third-order score o3gScore(g, h, s, m) from o3g model.


(24) ELIMINATION (twice) Fo(tabe'(sushi')(Ken')), Ty(t), ♢ Fo(Ken'), Ty(e) Fo(tabe'(sushi')), Ty(e→t) Fo(sushi'), Ty(e) Fo(tabe'), Ty(e→(e→t)) The above analysis readily solves our first set of the data on the syntactic flexibility of postposing.

postposing may take place at an embedded level.


The system is developed by combining and improving two previous algorithms (Tungthamthiti et al., 2014;Xu et al., 2015).

we propose a new method for coherence identification across sentences, some additional features indicating the strong emotion of the Twitter user, and several features of punctuations & special symbols that contribute to the final sentiment score.


it is interesting to notice that baseline 1 can achieve such results because the majority of the tweets are annotated with moderate negative values, varying from -2 to -3.


On top of the features derived from two previous well performing systems, we enriched the feature set with several new implemented ones.

the "additional features" is added to the module 1, while the counters of several punctuations & special symbols and a new method to identify "coherence feature" is proposed in the module 2.


The results achieved in each experiment are illustrated in terms of F-measure and macro-averaged Fmeasure in Table 7 Table 7 indicates that most of the selected ensemble classifiers have exceeded the results yielded by base classifiers in terms of macro-averaged F-measure.

majority voting of MaxEnt, SVM and ANN has achieved the best results with a macroaveraged F-measure of 85.06%.


(Clements 1975: 158-9 (Pan 2001: 291) While the logophoric pronoun yè in Ewe, one of the West African languages, in (2a) can be co-referential with either the matrix subject Ama or oblique Kofi, which functions as the source of the given reportive context, that in (2b) can only refer to Kofi with the thematic function of source, but not the first person pronoun me 'I'.

the sentence in (2b) is unacceptable when the first person pronoun me 'I' is an antecedent of the logophoric pronoun yè because the referent of a logophoric pronoun should be in the third person.


(Clements 1975: 152) The sentences in (8) and (9) have shown that the Ewe language makes a sharp distinction between indirect speech and direct speech.

the logophoric pronoun yè is exclusively used in the reportive context, as in (8), and the first person pronoun ma, which is the complex form consisting of the first person pronoun me and tense marker a-, as in (9), is normally used to refer to the external speaker in direct speech.


In other word, in (15a), the event is being described objectively.

the camera is placed at equal distance from both John and Mary.


The speaker describes (17) from Taroo's standpoint and (18) from Hanako's point of view.

the agent-centered verb yaru is used when the speaker empathizes more with the referent of the subject, whereas the beneficiary-centered verb kureru is used when the speaker empathizes more with the referent of the dative object rather than with that of the subject object.


Thus, the unacceptability of (34) is not due to the presence of the first person pronoun but due to the conflicting empathy foci.

there are two empathy loci in a single sentence.


Conflicting empathy foci trigger the blocking effect in Chinese as well.

the blocking effect is not attributed to the person feature mismatch, but to the empathy relationship between the participants in a given discourse context.


2012, but another factor Scrambling was also taken into consideration as in Kim and Goodall (2014).

the following three factors were employed in the experiment: Island constraint (Absence vs.


This paper took a significance-based approach.

the analysis would start from the maximally saturated model, and continued to remove predictors (backward) until the analysis reached the significant differences in the p-value (significant-based).


This implies that Korean may have Complex NP island effects through the interaction with other factors.

though the factor Island itself does not have statistically significant influence on the acceptability of the Korean sentences, if this factor interacts with other factor(s), it may have a statistically significant influence.


characteristic property of the referent of the MS. e. MSs are felicitous if they can be construed denoting a newsworthy entity.

the properties (3c-e) distinguish MSCs from single subject constructions (SSCs).


The current study is one of the few studies that adopt experimental methods to investigate the properties of MSCs.

this study focuses on two different types of MSCs mentioned in the literature -Possessortype and Adjunct-type MSCs.


Figure 1 demonstrates that the mean values for SSCs are much higher than those of MSCs.

the participants considered MSCs less acceptable than SSCs.


3 Character-based versus Word-based SMT The standards of segmentation between word-based and character-based English to Chinese translation are different, as well as the standard of the evaluation of them.

the test data contains words as the smallest unit for word-based SMT, and characters for character-based SMT.


To integrate CSLM more efficiently into decoding, some existing approaches calculate the probabilities of the n-grams before decoding and store them (Wang et al., 2013;Wang et al., 2014;Arsoy et al., 2013;Arsoy et al., 2014) in n-gram format.

n-grams from BNLM are used as the input of CSLM, and the output probabilities of CSLM together with the corresponding n-grams of BNLM constitute converted C-SLM.


We propose a method for automatically converting system utterances into those that are characteristic of designated personal attributes, such as gender, age and area of residence, to characterize agents.

we focus on converting sentence-end expressions, which are considered to greatly affect personal attributes in Japanese.


To reduce this cost, we propose a method for automatically converting utterances into those that are suitable for various characters.

the method automatically modifies 'how to say it' (i.e., linguistic expressions) without changing 'what to say' (i.e., contents of the utterances).


This filter removes candidates that are not allowed to be adjacent to a content word on the left of the original sentence-end expression.

the filter removes the candidates whose left adjacent POS is different from that of the  (te-kudasai) Table 5: Semantic labels that should be consistent before and after conversion original sentence-end expression.


We conducted two experiments to investigate the performance of our proposed method of converting sentence-end expressions.

we asked a subject to score the converted utterances from the two perspectives of (i) grammatical and semantic acceptability, and (ii) appropriateness for desired personal attributes.


The average scores of characteristic acceptability of the converted utterances were significantly higher than those of the original utterances (paired samples t-test; p<0.05) except for the case of 40 and over.

the scores for the cases of under 40, male, female, and western Japan drastically improved (by approximately 0.8-2.0 points) due to the conversion.


As previous studies show that the auditory domain is the main target of synaesthetic transfers (Ullmann, 1957;Williams, 1976), the near synonyms, 聲 sheng "sound" and 音 yin "sound" in Mandarin Chinese, should be good candidates for our research, which have been established to represent different semantic/cognitive focuses on the sound.

speaking, 聲 is concentrated on the auditory production, while 音 is focused on the auditory perception, although both can refer to the sound (Hong and Huang, 2004;Hong and Huang, 2005).


The other similar property is concerned with the priority of vision to touch when modifying these two synonyms.

vision is used more frequently to characterize 聲 and 音 than touch.


Therefore, there is a noteworthy difference on selective constraints for the near synonyms on synaesthesia.

speaking, 聲 receives more synaesthetic modifications than 音, and also employs higher symmetry on modifiers selection in each sub-domain.


Eventually, some interesting findings are obtained.

聲 and 音, as a pair of near synonyms in the auditory domain, have both similar properties and different characteristics on synaesthesia.


In general, we can conclude that auditory synaesthesia is sensitive to the cognitive dichotomy of production/perception in Mandarin Chinese.

synaesthesia can be predicted and determined by the cognitive modelling.


Indeed, it is very common for CFL beginners to go through a mapping exercise in their mind between the logographic characters and alphabet-like phonology, as well as semantics, in reading.

they attempt to associate form with sound and meaning when learning to read (Cao et al., 2013a;Xu et al., 2013;Shen, 2004).


There is little or no systematic grapheme-phoneme correspondence in Chinese script (Xu et al., 2013).

the basic Chinese writing units (i.e., strokes) are not mapped to phonemes (Guan et al., 2011).


(2005) found that the writing performance of beginning readers is strongly associated with their reading skills.

for native speakers who are exposed to Chinese in daily life and so have developed phonology-to-semantics link before formal schooling, their development in writing serves as a more significant contributing factor to their reading fluency than phonological awareness.


Since motor memories can last for a very long period of time (Shadmehr and Holcomb, 1997), this writing-related motor information can be additional assistance for the activation of visual information in the process of Chinese character recognition.

handwriting may pair the movement patterns, usually stroke sequencing through well-practiced writing (Parkinson et al., 2010), with the language stimuli, namely characters.


The descriptive statistics show that the response rate ranged from 0.05 to 1.00 (Table 7).

every word received a response by at least one learner.


In addition, as shown in Figure 2, the correct rate was evenly distributed.

no word was shown to be too easy or too difficult for dictation.


But how does the newborn go on from there to make sense of the torrent of novel input?

how does the newborn travel the long distance from very limited initial abilities to full language acquisition?


Therefore, the crying (voluntary crying) and cooing sound (coos & laugh) at this stage may be more related to infants' desires and emotions.

they start to use their abilities as communication tools to communicate with their caregivers.


Being adaptive is the most important character of the agents.

the agents in the model can learn from their environment and dynamically change their behaviors in response to their experience.


Infants cannot acquire the contents beyond the limit of LoC at a given age.

when consciousness does not exist or does not reach a certain level, a learning process cannot be activated in an infant.


In addition to the comparison of semantic preference, the matching of equivalents has to be verified when all the components that are necessary for the unit to function have been identified (Tognini-Bonelli, 2001).

equivalence also needs to be achieved at the level of the ultimate pragmatic functionthe semantic prosody.


(2) Among the five faculties employed by human beings to interact with the world-vision, hearing, smell, taste and touch-we hypothesize vision is consequential.

the visibility of human body parts is important for the selection of a body part to be used metaphorically.


In our modified MIP, we take the unit or morpheme 血 xie "blood" out of the compound 血 緣 xieyuan "relations by blood" and examine the semantic change occurring to the body part in the compound.

considering 血 , and other body part terms, as a lexical unit in compounds, we examine the basic contemporary meaning of these body part terms and their behavior in compounds.


Regarding how the qualia structure works, we draw out all the two-character compounds with the four body parts positioned in front of and behind the other characters respectively.

we examine how each of the four body parts behaves in the compounds of the following patterns: X 血 xie "blood", 血 xie "blood" X X 肉 rou "flesh", 肉 rou "flesh" X X 骨 gu "bone", 骨 gu "bone"X X 脈 mai "meridian", 脈 mai "meridian"X We go through all the clauses/sentences with these eight patterns of compounds, and check whether they are metaphorically used.


The finding not only accounts for the constraints which underlie the selection, but also facilitates the prediction of the behavior of the four body part terms in these uses.

the higher the visibility a body part is, the more possible it is to be employed metaphorically.


However, in the IHRC construction in (18), repeated from (8), all the argument positions are filled, including the object position.

the sentence is "closed" and appears to be type <t>.


In VPE, however, the little v hosting the feature [voi] is not included in the VPE site.


We depart from Kehler (2000), suggesting that a cause/effect relation as well as a resemblance relation requires syntactic identity in ellipsis, but that they are distinguished in terms of the category that undergoes ellipsis.

when a resemblance relation holds, the bigger category vP is a target of ellipsis.


Since the two clauses involved are non-parallel, one clause may relate not to another clause but to a constituent inside it.

it is possible that one clause may, for example, modify the constituent inside another clause.


Therefore, there is bound to arise a Case mismatch in both Pseudogapping and VP ellipsis in a resemblance relation that holds for (33) and (34).

voice mismatch for vP ellipsis in a resemblance relation is not permissible, because it always invites Case mismatch between an object element and its corresponding subject or vice versus, ultimately infringing on the syntactic isomorphism on ellipsis.


Recall that the island effects are understood as the effects on acceptability of processing both longdistance wh-dependency and island structure contained in a single sentence like (1d) above (see Fodor (1983), Stowe (1986), Kluender (1998Kluender ( , 2004, and more recently Hofmeister & Sag (2010) for the studies on L1 processing of whdependencies; Juffs & Harrington (1995;, White & Juffs (1998), Williams et al (2001), and Juffs (2005) for the studies on their L2 processing).

the combined effects of the two factors are greater (i.e.


We assumed that fixed effects vary for all participants and items for each island type.

we accounted for by-participant and byitem variations in overall acceptability ratings.


Crucially, we find that nominal categories in Chinese diverge upon individualization, which can be realized along either spatial or temporal dimension, as evidenced by the application of different types of classifiers.

event nouns and deverbal nouns allow temporal individualization only, while xingwei-marked nouns are exclusively compatible with spatial individualization.


1 Most of the examples in this paper are corpora data, of which the sources are indicated at the end of the sentences.

sC refers to sinica Corpus and CCL the corpus constructed by the Chinese Center of Linguistics of Peking University.


(SC) behavior DE adventure merchant 'those merchants who conduct the behavior of being pirates' 14ruo zhongjie danwei fei shu yiliao if agency unit not belong medical jigou, er jinxing yiliao institution but do medical_treatment xingwei… (SC) behavior 'If an agent is not affiliated to any medical institution but conducts medical treatment…' Based on the above two differences, we conclude that dongzuo-marked nominal and xingwei-marked nominal are of different types.

dongzuo-marked nominal is an event noun while xingwei-marked nominal tends to be an entity noun.


individualization along the spatial dimension and individualization along the temporal dimension.

when an individual classifier is applied to an entity noun, the individualized entities can be said to exist simultaneously in the world and their individual-hood is obtained through their spatial differences.


However, they differ in how they are individualized for enumeration.

we identify two types of conceptually different individualization, i.e.


Similarly, if we use DHWS-4-grams or higher order ones, the relative position of each word will be more specific.

according to a DHWS structure, for each word (node), its position (relative to the whole sentence) can be strictly determined by its preceding parent nodes.


We define the confidence value of transferring the value of m i to m j as follows: where | • | denotes the cardinality of the enclosed set.

for each document m j ∈ M te , we consider each document m i ∈ M tr , compute the corresponding confidence V C (m i → m j ), and then transfer the V al value of The denominator of Equation 4 is the number of posts in M tr for which the transfer confidence to m j is non-zero.


Another observation is that the accuracy in predicting non-news is higher than that of predicting news.

the probability of mistaking a news item as a non-news microblog is lower than taking a non-news item as news microblog.


As the two papers mentioned, the trouble descriptions in the training data were rare, less than 10%.

the ratio of positive and negative instances for this task tends to be biased.


In this paper, a TS needs to contain a product name/information and TE(s).

we do not handle any tweets without a product name/information.


However, most of real tweets are non-trouble information.

this situation is not on the real world.


To solve this problem, we need to keep high precision in the iteration process.

we need to reject noise TEs in candidate TEs.


More properly speaking, when the number of iteration was 5, our method did not obtain new TEs.

both of the two conditions in Section 5.1 were fortuitously fulfilled in this iteration.


The two parameters of the algorithm is set according to the result from the preliminary experiment to detect numerical spikes based on mean and standard deviation with sufficiently high F1-scores.

the parameters γ and s are set to γ = 1 and s = 2.


This is obvious for Supervised LM performing well on #momoclo hashtag because when testing on this hashtag, Supervised LM is built from the rest of five target hashtags including #agar and the suffix of the emotionally upsurging tweets from #momoclo are similar to that of #agqr.

those tweets include lots of "w"s which is an Internet slang meaning "lol (laugh out loud)" in English.


Our method is designed on the based on 'negative transfer.'

if the difference between class distributions of feature on the source domain and the target domain is quite big, we assign the feature negative weight (−1), In order to investigate the effect of negative weights here, we make an experiment which did not assign negative weight.


In this paper we propose a method for a rating prediction task with some aspects.

we focus on multi-scale and multi-aspects rating prediction for reviews.


However, all sentences in a review do not always contribute to the prediction of a specific aspect in the review.

the methods handling a review globally are not always suitable to gener-ate a model for rating prediction.


In addition, we distinguish words in the pros text areas and the cons text areas.

for a word w i , a word in the pros text areas is w p i and a word in the cons text areas is w c i .


The baseline did not use any aspect-sentence information.

it was based on (Shimada and Endo, 2008).


This is one reason that the MSE values in Section 4.2 were not sufficient.

owing to lack of textual information, the aspect rating prediction is essentially a difficult task.


We learn a set of users that represent each of these dimensions and use their ratings to predict product ratings.

we work with restaurant reviews to identify users whose ratings are influenced by dimensions like 'Service', 'Atmosphere' etc.


However, these events are annotated only when the spatial relationship is explicitly stated in a single sentence.

it does not consider implicit associations between general events and their spatial entities that are found across the text.


Both S1 and S2 refute that a nuclear explosion occurred.

the predicate "occur" is negated, even though no negation words (e.g., "not") are explicitly written in the sentences.


In the ablation test, the results indicate that every feature was effective.

lower performance was observed after removing each feature.


In this paper, we propose to detect suicide ideation in Chinese SNS and explore the possibility of using Topic Model (Blei et al., 2003).

we first collect and evaluate suicidal microblogs by psychological standards.


In addition, note that answers to polar questions can vary.

in languages like Korean and Finnish, polar questions can be answered by echoing the verb in the polar question: When the answer is a negative proposition, the prefix negator an is employed.


Kramer and Rawlins (2011) and Holmberg (2013Holmberg ( , 2016 suggest that the particles yes and no contain clausal structure that undergoes PF-deletion, assimilating them to elliptical constructions such as fragment answers.

holmberg (2016), adopting the movement-deletion approaches for elliptical constructions suggested by Merchant (2001Merchant ( , 2004 and subsequent work, suggests that the standalone answer particle as well as echoed verb are derived from clausal ellipsis as represented in the following: Accepting hamblin's (1973) view that polar questions are propositional abstracts (having the set of alternative propositions p and ¬ p), holmberg (2016) suggests that polar questions like (10) evoke the variable Pol with two possible values: (10) in turn then means 'What is the value of [± pol] such that the student came?'


Holmberg suggests that in languages like Cantonese or Korean, the negation is assumed to be within a VP so that it does not affect the Pol value.

the particle answer yes with the interpretation of yes, she is not diligent would not cause any feature clash in the polarity value.


The only difference lies in the semantics: The meaning of yes, as given in (21), is asserting the value 1 which is identical with the propositional meaning of the MAX-QUD, which is constructed from a polar question in the context.

the particle picks up the nucleus of the propositional meaning from the MAX-QUD and asserts it.


As we have seen, a key difference from English arises from answers to a negative question: Different from English, the affirmative particle ung 'yes' confirms the negative proposition, not the positive proposition.

in the Korean system the answer to a negative question confirms or disconfirms the truth of the negative proposition, one key property of the truth-based system.


The TOTAL is the total duration that the gaze spends within the area of interest.


Information that the speaker thinks the hearer already knows is hearer-old, and information that the speaker does not think the hearer already knows is hearer-new.

hearer-old information is common ground for both the speaker and hearer, and hearer-new information is not.


ka changes force from DEC to INT, introducing the felicity condition ¬B c S ϕ.


ATCs assert that the nonstandard use of such lexical items frequently occur during Route or En-route Clearance while Pilots confirm that these transpire during Takeoff Clearance, Altitude Clearance, Approach Clearance, and Landing Clearance.

the nonstandard use of Aviation Phraseology in this study shows nonconformity in the efforts of the International Civil Aviation Organization to provide "maximum clarity, brevity, and unambiguity".


Although there is a need to revisit Kachru's three-circle model in this regard, it is still vital to be taken into account since pilots and ATCs either native or nonnative speakers of English coming from different nations speak different varieties of English.

there is a need to understand the World Englishes paradigm and use it as a theoretical underpinning in describing the lexical items in standard phraseology having non-standard definition.


We propose a new approach to stylometric analysis combining lexical and textual information, but without annotation or other pre-processing.

our study makes use Chinese tones motifs and word length motifs automatically extracted from unannotated texts.


d. The value of DOM is a list of elements of type NODE, which consists of the features PHON and SYNSEM (Pollard, Kasper and Levine, 1993).

(11c) is described by the sequence union relation: Z is a list obtained by merging X and Y with the condition that the relative order of elements in X and Y is preserved in Z.


Participating systems have approached this task by both traditional and deep learning architectures.

the first-rank system of the VLSP campaign which achieved an F 1 score of 88.78% used MEMM with many hand-crafted features (Le-Hong, 2016).


It is because LSTM uses memory cells instead of hidden layers in a standard RNN.

there are three multiplicative gates in a memory cell unit that decides on the amount of information to pass on to the next step.


Named entities are labelled by the IOB notation as in the CoNLL 2003 shared tasks.

there are 9 named entity labels in this corpus including B-PER and I-PER for persons, B-ORG and I-ORG for organizations, B-LOC and I-LOC for locations, B-MISC and I-MISC for other named entities, and O for other elements.


The performance is increased from 71.74% to 74.02%   Table 6: Performance of our model with and without dropout Syntactic Features Integration As shown in the previous experiments, using only word features in deep learning models is not enough to achieve the state-of-the-art result.

the accuracy of this model is only 74.02%.


An explanation for this problem is possibly a characteristic of Vietnamese.

vietnamese named entities are often a noun phrase chunk.


appear to the right of the relative clause) as in (11), this is not the case with NP 2 imouto 'younger sister' and NP 3 kami 'hair' as shown in (12)-(13).

it is (at first glance) unexpected that (22), where NP 2 GDP in (20) occurs to the right of the clause, seems fine.


The natural difference between their works and our work is as follows: In (Neubig et al., 2012;Nakagawa, 2015), they train an incremental parser for preordering, following the order in the target language before decoding, but we do reordering while decoding.

we adopt their model but make use of it as an online reordering heuristic during decoding.


In decoding, we follow (Och and Ney, 2002;Chiang, 2007).

we remove the target side and use a more general linear model composition over 2 https://github.com/wang-h/HieraParser 3 We skip the sentences which cannot be parsed under the constraints of BTGs.


Other factors combine to make the identification of NE more challenging.

the use common names as parts of name or surnames or the use of prefixes like (Abd) (servant) associated with a name that describes God.


The results for both PHQ-2 and CESD-R should be the same.

if a user scores depressed on the PHQ-2 scales, the user must also score similarly on the CESD-R scale.


We try to incorporate part-of-speech features in recurrent neural network language model, and use them to predict the next word.

we proposed a parallel structure which contains two recurrent neural networks, one for word sequence modeling and another for part-of-speech sequence modeling.


We may also regard the "newly" discovered [tʃ] as an allophone of /ts h / in the Cantonese inventory, especially for the 20s generation.

the substitution of /ts h / could be regarded as a sound change /ts h / /ts/ > /tʃ/ in the group of young speakers.


In (Ng and Zue, 2000), an analysis of news text demonstrated that the vocabulary size would continue to grow as the dataset got larger.

it was not possible to create single large vocabulary that would eliminate the out-ofvocabulary problem.


In this case, we can only conjecture that this is attributed mainly to the typical property of caki to refer to an attitude holder.

korean caki functions as a logophor in the majority of cases unless particular clues are provided in the discourse.


The aim of this paper is to discuss denominaled adjectives in light of recent advances in linguistic theory and make the following claims about their structure, morphology and semantics.

on the fundamental assumption in the framework of Distributed Morphology (Halle and Marantz, 1993;Marantz, 1997Marantz, , 2001) that there is no component dedicated to word formation, this paper defends the view that the -ed adjectives in question are denominal and argues that bases fored are reduced nominal structures, nP.


11 (14) a. beautiful-kimonoed b. three-footed c. raidused d. antennaed e. two-axised f. slow-tempoed Furthermore, the nP-based analysis works well with pluraria tantum nouns, with ancillary assumptions.

i follow Arregi and Nevins's (2014) analysis of pluralia tantum nouns, where these nouns are assumed to have their n head specified for [−group], and, if Num is present in structure, they must appear with the Num head specified as [−singular].


Building on Bauer and Huddleston's insight, I argue that the lexicalized adjectives above are amenable to a different analysis.

i argue that they are stative participles in the sense of Embick (2003Embick ( , 2004 and that syllabic -èd in these adjectives is a case of contextual allomorphy determined by the Root involved.


18 The complex head in (18)b is created in the structure in (18)a, and it undergoes VI from the Root out.

for the lexicalized cases above, I argue that they are derived by the VI rule in (19)a below, which Embick (2003) proposes for stative participles like allegèd, blessèd, and learnèd.


unpredictable from their putative nominal bases.

as we saw at the outset, the meanings of denominal -ed adjectives are predictable from their nominal bases fairly easily, construed as 'having N' or 'provided with N', etc.


However, leggèd and related forms appear to resist an explanation along the line suggested here.

22 on the assumption that syllabic -èd appears as a result of Root-determined contextual allomorphy, leggèd is predicted not to appear in denominal adjectives owing to the VI rule in (19)b.


However, the two cannot be equated even if the categorial difference is taken into consideration.

the adjectivizing suffix and the possessive D are crucially different in that the latter, which is semantically transparent as in (25)a below, can undergo type-shifting so that it can accommodate non-relational possessees, while the former cannot.


I argue that this is possible because this class of nouns are coerced into relational nouns.

by building on the insight of Vikner and Jensen (2002), I argue that they can undergo type-shifting on the basis of their telic function (i.e., clothes are to wear), as a result of which the concomitant meaning-shift operation derives the relational denotation in (29) from the original, non-relational one: λx[CLOTHES(x)] (where CLOTHES stands for any noun denoting clothes or accessories).


However, the Possessor-of-Subject GR is not representative of how non-Subject NPs behave, since it has sometimes been argued to have prominence over the Subject.

the Possessor-of-Subject can scope or bind out of the Subject in certain circumstances, as noted by Kayne (1994).


These initial observations led to the consideration of the chief gesture of stylistics that is to closely examine the 'linguistic particularities of a text' that leads to the 'understanding of the anatomy and functions of the language' (Toolan, 1998, p. ix).

it is significant to pay attention to the language in the text to gain understanding and meaning of the literary piece because 'literature is made of language' (Watson & Zyngier, 2007, p. xii), and stylisticians uphold this principle for several years.


It is an exhaustive data analysis of CLC on two SFPs.

native speakers can demonstrate the usage of two SFPs in different situations precisely, which proves that the two SFPs have obvious differences on word sense.


The main purpose of this study was to determine the pedagogical viability of integrating 'closed' Facebook groups in the ESL classroom.

the study attempted to answer the following questions: (1) How often do students access the class' Facebook groups, and what features of Facebook groups do they use during their access?


It is also important to note that the scalar reading is also possible from 这么重要的面试' such an important interview' .

the expectation being that this interview is the one that the subject (he) can least affords to fail.


And with a slightly different focus (and background information), the expectation can also be on the subject 他 he.

if the subject is sent by a bidding team to represent them at the important final interview (instead of other team members).


Moreover, to create a valuable corpus, it is important to annotate the promotion/suppression relations involving the article title (a variable T, hereafter), because the article is naturally intended to provide knowledge about T. Therefore, we force T to participate in an argument of a promotion/suppression relation.

the annotation task is accomplished by labeling PRO ("T promotes Y"), SUP ("T suppresses Y"), PRO BY ("X promotes T"), or SUP BY ("X suppresses T") for text spans (denoted by Y for PRO and SUP, and denoted by X for PRO BY and SUP BY) in the article.


As shown in the figure, the highest consistency was achieved in 2-match aggregation.

spans should be aggregated when two or more annotations are exactly matched.


Suppose that causal labels PRO, SUP, PRO BY, and SUP BY are annotated 4000, 3000, 2000, and 1000 times in a corpus, respectively.

the annotation ratios of the PRO, SUP, PRO BY, and SUP BY labels are 40%, 30%, 20%, and 10%, respectively.


Thus, this study aims to investigate the factors and explore on different methods that will affect the process of building a Tagalog document classifier.

this study intends to: • Collect Tagalog news articles and label them according to their category • Represent and extract features from documents using NLP techniques • Build an SVM Classifier • Evaluate classification performance and present results Different researchers have already explored on automatic document categorization to help manage documents efficiently.


If the variation difference in transitivity exists, is this variation dependent on the degree of lexicalization?

whether the variation differences in transitivity indicate the different stages that VO compounds from different variants are located in the continuum/process of lexicalization?


It should be noted that previous studies usually examine both separation status and transitivity issue in a dichotomy way.

the VO is classified as separable vs. inseparable, transitive vs. intransitive (e.g., Gao, 1998;Her, 1996 among others).


The results of spearman's correlation in both Taiwan and Mainland show that for a VO compound, the more frequently it is used separately, the less likely it can be used as a transitive verb.

for a VO compound, the more it is lexicalized, the more likely it is used in a transitive way.


And also, for a VO compound, the less frequently it is used separately, the more likely it is used as a transitive verb.

if a VO sequence is less lexicalized, its probability of being transitive is higher.


If the answer is yes, does this transitivity difference is depend on the process of lexicalization of these VO compounds?

do the differences of transitivity and separation between Taiwan and Mainland Mandarin indicate the different stages that Mainland and Taiwan VO compounds are located in the continuum/process of lexicalization?


But ones thing should be noted is that the P value cannot tell us everything.

among the 76 words which show significant difference in transitivity frequency between Mainland and Taiwan, their degree of difference varies.


Therefore it is possible for us to argue that the differences in transitivity frequency and separation frequency between Mainland and Taiwan Mandarin actually indicate the different stages that Mainland and Taiwan VO compounds are located in the continuum/process of lexicalization.

if the status of lexicalization is considered as a continuum from phrase to word, then compared to Mainland VO compounds, Taiwan VOs behave more like words instead of phrases, therefore it is more likely for the Taiwan VO sequences to be used in a transitive way.


if a VO sequence is less lexicalized, its probability of taking an object is higher.


Enlarging the training data (increasing from 130k to 456k parallel sentences) improved both SMT and NMT models.

the SMT model achived +0.65 BLEU point on the tst2013 and +0.99 BLEU point on the tst2015.


Regarding the sense ID as the part of speech, all-words WSD is a same problem as part of speech tagging.

to solve all-words WSD, we make a directed graph like Figure 1, and then estimate the optimum path from the start node 'S' to the end node 'E'.


Among 10,000 test instances, Ky-WSD correctly segmented 9,935 target words, and correctly assigned 6,258 senses to them.

the precision is 0.6571, the recall is 0.6528, and the Fvalue is 0.6549.


As a result, among 10,000 test instances, the new KyWSD correctly segmented 9,938 target words, and assigned 6,986 correct senses for them.

the precision is 0.7030, the recall is 0.6986, and the F-value is 0.7008.


In document classification, a document is translated to a vector using a bag-of-words model.


Furthermore, it seems that we cannot distinguish or predict the contexts of Tone 3 and Tone 4.

words with these two tones individually could occur in syllables with short or long nuclei (i.e., one or two vowels).


Second, in my current data, the contexts in which each tone occurs is not the same.

words with Tone 21 always have at least two vowels in the syllable, while the words with Tone 1 could have one, two, three or even four vowels in the syllable.


Second, tones can distinguish singulars from plurals grammatically (See Table 3).

singulars are different from plurals only in tone values (cf.


In phrases or sentences Fourth, tones seem to be able to encode the presence of a direct object (see Table 5).

when a direct object becomes explicit, the tonal value of the main verb changes and all breathy segments become non-breathy.


If it is predicable, perhaps it is possible that this tone sandhi rule interacts with plurality.

this tone sandhi happens only when both N1 and N2 are singular.


In this paper, we present a multi-dialect neural MT (NMT) system tailored to Japanese.

we employ kana, a Japanese phonetic lettering system, as basic units in the encoder-decoder framework to avoid the following: ambiguity in converting kana to kanji (characters in the Japanese writing system); difficulties in identifying word boundaries especially for dialects; and data sparseness problems due to handling large numbers of words originating from different dialects.


In order to capture word senses at a finergrained level, we propose a method to consider context words as a clue to distinguish word senses and generate representations for each pair of word and its context word.

we regard words with dependency relations in a sentence as context words.


In the proposed method, we need to deal with the data sparseness problem because there are a number of word combinations among target and contextwords.

we take the following two measures to address this problem.


Hence, we expect that more accurate similarity can be computed by restricting pairs of representations to these based on the same context-word.

avg is reformalized as avgc as:  Based on the final similarity scores, we rank the candidates in descending order of the similarities.


In this paper, we proposed methods that generate multiple word representations per word in order to capture its senses.

we assume that the senses of a word can be captured by context-words with dependency relations in a sentence.


In this paper, we focus on a few dimensions whose development seems more particularly to the automatic understanding of Arabic texts.

we introduce a real analysis of text passages which may contain an answer to a question.


The logical forms generated from the conceptual graphs are taken into consideration during the textual entailment determination step that relies on the semantic and the logic representation to extract the desired answer.

[Sowa, 1984] defines an operator Φ (Phi) that transforms every element of a conceptual graph model into an element of first-order logic.


Working with user-generated data such as those found in Facebook poses some challenges, as they tend to be noisy (Petz et al., 2013;Dey and Haque, 2009;Abbasi et al., 2008) in nature.

texts are expressed in informal language and incomplete sentences.


First of all, no significant findings were found for syntactic roles.

both disciplines exhibit similar pattern in how predicative adjectives are used in complementation.


(2017) revisited the approach from the perspective of neural machine translation (NMT), without reference to any underlying grammar or creating any phrase table.

pivoting is done with the NMT model, in the form of oneto-one back-translation or multi-pivoting through the K-best translations.


To obtain the formula, we first estimated the step where the identification rate was 50% and designated it as a cut-off point because a step greater than this cut-off point meant that it was less likely to be perceived as a level tone.

the cut-off step is the smallest number of steps where a rising or falling pitch direction was perceived as different from a level tone.


For English non-musicians, a linear regression without a quadratic term could be used to model the sharpness of category boundary with respect to duration (χ2(1) = 0.12, p = 0.73) and yielded good predictions (F(1, 34) = 38.26, p < 0.001; adjusted R 2 = 0.52) (Chen et al., 2017).

the formula for the relationship between the sharpness of category boundary (b1) and duration (d) for English musicians is presented in Eq.


For English musicians, a regression model with an extra quadratic term turned out to be significantly different than the model with only the slope and intercept terms, as suggested by a likelihood ratio test (χ2(1) = 8.75, p = 0.003).

the formula for the category boundary (cb) and duration (d) is listed in Eq.


Our results show that increasing rate of category sharpness differs between musicians and non-musicians.

although category boundary sharpness increased with duration among both English musicians and non-musicians, English musicians showed a faster increment than nonmusicians, indicating that musical experience draws greater benefits from extra stimulus duration.


We also found that a falling pitch on a high vowel requires less time to identify that on a low vowel because its perceived slope is shaper on a high vowel than on a low vowel.

while the onset values of a falling pitch on a low and on a high vowel is perceived to be equal, its offset values are perceived to be relatively lower on a high vowel than on a low vowel.


The automatic evaluation by linguistic test points shows the similar results in the BLEU and NIST.

bLEU and NIST of NMT2 in English-to-Korean NMT systems were higher 0.1183 and 1.4093 than those of NMT1, respectively.


In this work, we hypothesize that task specific supersense could also be utilized as an informative feature for relation classification.

we define a new entity type based on the property of a given task, and facilitate scientific relation classification with the task specific supersense.


To address this issue, in this work, we propose a minimally supervised approach that utilizes supersense embeddings.

we manually prepare a small number of seed instance words for the predefined supersense (or TSS) (e.g., "survey" for RESEARCH-PROCESS) and train the embedding of word and supersense in the same vector space, like the method Flekova and Gurevych (Flekova and Gurevych, 2016) proposed, which will be detailed in Section 3.


Since the TSS is positioned in the same vector space with original words, we could utilize the embedding cosine similarity between TSS and given words to determine their TSS.

we tag a given word with the TSS, if the cosine similarity is above a predefined threshold score 5.


For testing this hypothesis, we compare the performance of TSS enhancement with the base model.

we compare the performance before-and-after the automatic TSS tagging, which is mentioned in Section 3.


This could be because given a specific RC task and its corresponding dataset, some TSS might be redundant when classifying relations.

without the external information from TSS, only the internal information from the dataset itself (e.g., the hint word "using" in Example 9) could be enough to identify some relations (e.g., USAGE(X, Y) in Example 9).


The performance of such systems is generally suboptimal, because the answers are generated through the assumption that exactly one question is embedded within one complete utterance.

the entire utterance is processed as a single question.


emb() is the target side embedding matrix which converts sentence labels in real-valued continuous vectors.

at a given time t , the sentence selector reads sentence s t from document D and predicts how worthy is the sentence for inclusion in the summary S, conditioned on the document representation (obtained from the document encoder) and the label predictions made for the previous sentences in the document.


For example, given the following sentence as input: Many methods for NER are based on supervised learning.

conditional Random Field -cRF (Lafferty et al., 2001;Sutton and Mccallum, 2006) and Long Short Term Memory -LSTM (Hochreiter and Schmidhuber, 1997) are the most popular methods.


We checked the effectiveness of each feature for NER accuracy in each model.

we integrated Character language model that (Liu et al., 2018) and (Akbik et al., 2018) proposed, with our system (LM-LSTM-CRF).


For a given query Q, containing a variety of logical operators, our algorithm treats each operator type alone.

it separates the conjunction operators from those of disjunction or negation.


Most of the studies indicate that exclamative sentences contain strong emotions (Li, 1924;Gao, 1948;Lv, 1942;Huang, 1958;Zhu, 1982).

exclamative sentences are considered to involve unexpectedness, i.e., a sense of surprise (Milner, 1978).


The present study attempts to provide a look at how much onset age and number of years of study can influence the acquisition of an L3 grammar construction that shows some degree of similarity to both the L2 and the L1.

it looks at the acquisition of progressive constructions in the L2 French and L3 English of L1 Burkinabe language speakers.


Progressive constructions were examined in this study because the progressive/perfective aspectual system shares a certain degree of similarity between French, English, and Burkinabe languages.

the perfective aspect is marked in the same way in French and English, but differently in Burkinabe languages, but the progressive aspect is more similar between Burkinabe languages and English for present progressive marking.


Even though argue and chitchat are both members of 'chitchat verbs', ARGUE is polysemy in nature.

since the word sense of ARGUE in (2.2b), expressing a proposition, is not covered in 'chitchat verbs', ARGUE could take a sentential compliment regardless the syntactic constraint in 'chitchat verbs'.


(AC1987) Then, within the clause types, we further annotated the finite clauses -'that clause' (that-C) in (3.3a), 'wh-clause' (wh-C) in (3.3b), and whether/ifclause (whether/if-C) in (  Although core FEs are required at the semantic level by their frames, in some circumstances, they may be absent at the surface level.

they are not presented in the sentence, but exist at the semantic level.


The with phrase refers to the one(s) holding different opinions contrary to 'Arguer1'.

almost half of the sentences indicate different opinions between 'Arguer1' and 'Arguer2'.


By not specifying the arguer, nonreferential-it implies that the content should be well-recognized.

since the user considers or assumes the content to be wellaccepted, there is no need to specify the exact arguer but use non-referential-it.


Among all the 'Issue's in the frame, while 82.5% 'Issue's are absent with ['Arguer1'+'Arguer2'], fewer 'Issue's (54.1%) are missing with 'Arguers'.

'Issue' prefers to co-occur with 'Arguers' rather than ['Arguer1'+'Arguer2'].


By contrast, F0 on the classifier appears higher for both NP-FOCUS and NUM-FOCUS (NU) than for NOUN-FOCUS (NO).

it appears to pattern with the F0 level of the numeral.


An alternative explanation could therefore be related to how focus marking interacts with pre-existing prosodic boundaries, or else the tendency for boundaries to be strengthened at specific locations as a result of focus Finally, we found that the classifier did not pattern with respect to focus in the same way across the three acoustic measures.

its duration was not affected by focus condition, but it showed a higher mean F0 and greater intensity in the Num-Focus and NP-Focus conditions as compared to Noun-Focus.


Since many recent approaches to computational metaphor processing have used context-based features as input to the classification task (Shutova et al., 2012;Jang et al., 2015;Jang et al., 2016), the degree of success will largely be influenced by which part of the above life cycle the metaphor is in.

depending on the degree to which the metaphor is conventionalized in the language, it is expected to be far more difficult to detect using computational methods, since its domain essentially becomes indistinguishable from the rest of the context.


As output, a 600-dimensional attention layer was included prior to the final output, allowing the model to learn what to focus on from a sequence overall, based on a weighted combination of all the input states to the layer.

α it , the particular 'importance' of a word vector, is calculated through the activation of the hidden layer for that word, u it , and a learned context vector, u w , input through a softmax function.


The definition of a personality suggests two important things that a human or a system needs to understand: behaviors and their psychological reactions.

behavior and subjectivity are the critical determinants.


An important issue with incremental interpretation, from a formal semantic viewpoint, is that sentence prefixes do not have propositional interpretations (Chater et al., 1995).

standard formal semantics cannot be applied to incremental interpretation directly.


Dialogue systems have been increasingly important these days.

non-taskoriented dialogue systems are studied because of the success of neural network approaches such as seq2seq models.


Dialogue systems, such as Siri and Alexa, have been increasingly important these days.

non-task-oriented dialogue systems are studied because of the success of neural networks or reinforcement learning approaches such as seq2seq models (Vinyals and Le, 2015;Li et al., 2016).


As mentioned above, zero-pronouns frequently appear in Japanese sentences.

sentences do not always contain the subject.


Therefore, we use the IDF value of the subject as the feature value.

we assume that the trivia score of a sentence is high in the case that the IDF value of the subject in the sentence is low, namely a well-known word.


MAE MSE R2 Proposed 0.2490 0.0835 -0.0108 Baseline 0.2503 0.0827 0.0000 As a result, we obtained a balanced dataset.

the trivia score of the top 103 trivia sentences 10 was 0.95 and that of the next 103 trivia sentences was 0.85.


In addition, our method estimated that the trivia scores of most sentences were within the range of 0.45 to 0.65.

the range that our method can estimate is insufficient.


sentences with high trivia scores for the purpose.

we need trivia sentences in the higher rank in all sentences.


This result shows that the proposed features were effective for the ranking task, as compared with a scoring function from the related work.

the proposed features were effective to recognize a relationship between magnitudes of trivia scores although those were not sufficient to estimate actual trivia scores of sentences.


However, it should be in ascending order from the assumption described in Section 4.2.1.

this was a reversal phenomenon; we expected that the values in the top ranks and the bottom ranks were low and high, respectively.


3.1 Relative lack of use of markers 着 zhe and 过 guò in SM and possible reasons When examining the relative frequencies of individual aspect markers across the three varieties, it is noted that SM uses much less of the markers 着 zhe and 过 guò than in the other two varieties.

outstanding is in 过 guò between SM and MCM, where the difference is as high as nine times.


This may be an indication that MCM has been taking in features from non-MCM varieties just as these non-MCM varieties lean in towards MCM.


(2012) also find that 了 le is typically attached to the second verb of a serial verb construction, which is also typically the head of the sentence.

the aspect marker 了 le has to be typically attached to the second verb when it is functioning as the head.


Thus, we develop a novel method for training token-wise correspondence model that takes unsupervised learning approach.

we minimize sentencelevel loss instead of token-wise alignment loss.


These examples also demonstrate that SPM successfully reduces odd-gen. We visualize the prediction of the SPM and the attention distribution to see the acquired token-wise correspondence between the source and the target.

we feed the source-target pair (X, Y ) to EncDec and EncDec+SPM, and then collect the source-side prediction (q 1:I ) of EncDec+SPM and the attention distribution (α 1:J ) of EncDec 12 .


This function calculates the attention score in bilinear form.

the attention score between the i-th source hidden state and the j-th decoder hidden state is computed by the following equation: where W α ∈ R H×H is a parameter matrix, and α j [i] denotes i-th element of α j .


(2017) is the state-of-the-art summarization model on the 1 https://github.com/KodairaTomonori/ ThreeLineSummaryDataset/ CNN / Daily Mail datasets.

their baseline model is based on the one proposed by (Nallapati et al., 2016), which is further improved by including a hybrid pointer-generator network and coverage mechanism.


(2017) also proposed a hybrid pointer-generator network, which combines the attention and vocabulary distributions.

their pointer-generator network is a hybrid between a sequence-to-sequence attention model (Section 2.2) and pointer network (Vinyals et al., 2015).


These summaries are written by human editors.

these summaries consist of exactly three sentences, which we will discuss in detail later.


However, the third sentence plays various roles in this dataset.

in a sequence summary, the third sentence is based on the second sentence, whereas in a parallel summary, the third sentence is based on the first sentence.


In Section 2.2, we describe the base summarization model (See et al., 2017).

we pre-train the structure-specific summarization models using all training data as in (See et al., 2017) regardless of the summary structures, and then perform fine-tuning for each type using the automatically annotated dataset.


Because the summary structure is biased, as indicated by the results in Table 1, we optimize our model by under-sampling to achieve high precision.

we sample data for each label until its precision exceeds 0.8.


We annotated and analyzed the structure of the summaries in the considered dataset.

we proposed a structure-aware summarization model combining the summary structure classification model and summary-specific summarization sub-models.


This study aims at attesting this phenomenon with a corpus-based approach and further investigating the functional changes of demonstratives with respect to different speech situations.

we compare the use of demonstratives in three Hong Kong Cantonese corpora of spontaneous speech, for two types of conversations and interpreted speeches.